VLDB 2026 Research / reviewers in the wild / expert
Evander Kristalino
dblp:279/2836
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Vehicular, aerial and satellite networks · 87% Internet of things and sensor networks · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Vehicular, aerial and satellite networks
intelligent transportation systems |
0.4 | 1 | 2020 | A sensor-based application for road conditions detection: poster abstract · SenSys 2020 |
Vehicular, aerial and satellite networks › intelligent transportation systems
road condition detection |
0.4 | 1 | 2020 | A sensor-based application for road conditions detection: poster abstract · SenSys 2020 |
Internet of things and sensor networks
wireless sensor network |
0.1 | 1 | 2020 | A sensor-based application for road conditions detection: poster abstract · SenSys 2020 |
Methods — techniques the papers use, named apart from their topics
k-means clustering · 0.4gyroscope · 0.4accelerometer · 0.4GPS · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | A sensor-based application for road conditions detection: poster abstractabstractRoad conditions affect the safety of road users as potholes may cause traffic accidents. We design and implement a sensor-based application for road conditions detection. The sensor device includes a GPS NEO-6M module to get the pothole locations and an MPU-92/65 module as accelerometer and gyroscope sensor, which are attached to a NodeMCU ESP8266. We use REST API as the web service to connect the sensor device and users, who will access the information using the Android application on their smartphones. To cluster the acquired data, we use the k-means clustering algorithm. The algorithm clusters data into deep, medium and shallow potholes. Lanny Sitanayah, Apriandy Angdresey, Evander Kristalino |
SenSys | 3 |